Patent Corpus Mining

Patent Corpus Mining is a computational analysis methodology that systematically extracts, identifies, and interprets patterns, relationships, or novel information from large-scale collections of patent documents to support innovation discovery, technological forecasting, or intellectual property analysis.

138

Publications

7.4K

Citations

444

Authors

207

Institutions

Publications per year

2017–2026

62

Authors

444

  1. GF
    Gualtiero Fantoni

    University of Pisa, Italy

    5 publications · 252 citations

    5

    h-index

  2. FC
    Filippo Chiarello

    University of Pisa, Italy

    4 publications · 161 citations

    4

    h-index

  3. CV
    Charles V. Trappey

    National Yang Ming Chiao Tung University, Taiwan

    4 publications · 252 citations

    4

    h-index

  4. AJ
    Amy J.C. Trappey

    National Tsing Hua University, Taiwan

    4 publications · 252 citations

    4

    h-index

  5. SL
    Sungjoo Lee

    Ajou University, South Korea

    3 publications · 318 citations

    3

    h-index

1–5 of 444

Venues

  1. World Patent Information

    22 publications · 758 citations

    15

    h-index

  2. Technological Forecasting and Social Change

    11 publications · 884 citations

    11

    h-index

  3. Scientometrics

    10 publications · 432 citations

    10

    h-index

  4. Expert Systems with Applications

    7 publications · 263 citations

    7

    h-index

  5. Advanced Engineering Informatics

    4 publications · 267 citations

    4

    h-index

1–5